CN117950423A - A collaborative fire fighting system and method based on drone - Google Patents

Abstract

The invention relates to a collaborative fire control system and a collaborative fire control method based on an unmanned aerial vehicle. A plurality of unmanned aerial vehicles are arranged in the unmanned aerial vehicle control station. The unmanned aerial vehicle control station and/or the unmanned aerial vehicle are/is connected with the computing unit in a communication way. The computing unit is configured to: pre-simulating a plurality of fire positions in the three-dimensional virtual scene to pre-plan a moving route between the unmanned aerial vehicle and the plurality of fire positions based on the three-dimensional virtual scene and fire-fighting materials or monitoring equipment carried by the unmanned aerial vehicle; the relevant travel routes are sent to the drone or drone control station in a manner that imparts fire tasks to the drone. According to the invention, the possible fire positions and the fire-fighting tasks to be executed by the unmanned aerial vehicle are simulated in advance in the three-dimensional virtual scene, so that the unmanned aerial vehicle can be deployed for early fire-fighting work at the first time of fire occurrence. The calculation unit directly calls the simulation data of the unmanned aerial vehicle movement to control the unmanned aerial vehicle to perform corresponding fire-fighting tasks.

Description

A collaborative fire fighting system and method based on drone

Technical Field

The present invention relates to the field of unmanned aerial vehicle (UAV) firefighting technology, and in particular to a UAV-based collaborative firefighting system and method.

Background technique

UAV, also known as unmanned aerial vehicle, is an unmanned aircraft that is remotely controlled or autonomously flown by a remote control station. In recent years, the development of UAV technology has become increasingly mature, gradually showing the characteristics of lightweight and intelligent, and is widely used in agricultural production, fire rescue, remote sensing mapping, police security, mass entertainment and other fields. UAV firefighting refers to the use of drones as carriers, carrying various equipment and modules, to achieve functions such as fire scene reconnaissance, fire extinguishing agent delivery, rescue material delivery, communication relay, etc., to assist firefighters in firefighting and rescue. The existing technologies of UAV firefighting mainly include the following aspects:

There are many chemical equipment in the factory. Once a fire occurs, it will spread rapidly to all parts of the factory. Early firefighting after the fire is an important factor in whether the fire in the factory can be controlled. The prior art has already used drone firefighting to carry out firefighting operations, but due to the different equipment and modules carried by the drones and the different types of drones themselves, the flight time and load of the drones are limited. How to dispatch several drones according to the type of drone, load and fire location is a problem that the prior art urgently needs to solve. In addition, when several drones perform firefighting tasks, the amount of data processing required is limited, which makes it difficult for several drones to obtain and execute the remote control instructions of the remote control station in a complex fire environment in a timely manner. The drone cannot complete the firefighting task autonomously. How to give the relationship between the remote control instructions of the remote control station and the firefighting task performed by the drone and how to improve the drone's temporary response ability to fire changes are technical problems to be solved by the present invention.

The patent application with publication number CN106828899A discloses a rescue drone and its control and fire-fighting system, which includes a drone with more than three axes driven by corresponding motors, an execution module arranged on the drone, and a control terminal that controls the drone and the execution module wirelessly. The patent uses several drones equipped with different fire-fighting materials or monitoring equipment to carry out dangerous rescue. However, the patent does not take into account how to coordinate and orderly rescue planning for many drones equipped with different fire-fighting materials or monitoring equipment, and how to form a continuous fire-fighting process in a cycle, resulting in poor applicability of this method. Especially for factory fire accidents that are difficult to extinguish in the middle and late stages, if simple drone deployment cannot effectively suppress the development of the fire in the early fire-fighting process, it is easy to miss the golden time for fire suppression or even extinguishing.

The patent application with publication number CN112774073A discloses a multi-machine cooperative fire-fighting method guided by a drone, which includes: starting the ground control terminal, the drone performs a flight search along the search waypoint and establishes ground two-dimensional map information, finds the fire point and accurately locates it, calculates and marks the spatial position information of the fire point, the drone and all fire-fighting unmanned vehicles, and all fire-fighting unmanned vehicles drive in formation towards the fire point after path planning, autonomously avoid obstacles and establish a three-dimensional sparse point cloud map, and judges whether to move forward or not through a temperature sensor, and starts the fire-fighting task near the fire point. After completing the fire-fighting task, the drone and all fire-fighting unmanned vehicles return along the original route. In this patent, the drone only performs the guidance work. If the drone cannot form a large-scale and low-cost drone swarm, it is only used as a fire survey machine, which greatly wastes the fire-fighting advantage of early firefighting. Especially for fire accidents in factories, it may be caused by chemical leakage. Since the fire-fighting unmanned vehicle needs to drive in the factory safety passage, it will directly contact the chemical products. If the chemical product comes into contact with the ignition equipment on the firefighting unmanned vehicle (such as fuel generators, electrical sparks, etc.) or the ignition equipment on non-firefighting unmanned vehicles (such as friction of rotating parts such as machine bearings, collision of iron objects, or tools hitting concrete floors, etc.), the fire will spread quickly and damage the firefighting unmanned vehicle. The patent does not consider the corresponding dispatch of drones based on the cause of the fire. The complex causes of fire in chemical plants make this guidance method unsuitable for chemical plant fires.

In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology.

Summary of the invention

Due to the presence of various toxic, harmful and flammable chemical materials and a large number of overhead equipment inside the factory, there are relatively complex fire conditions inside when a fire occurs. In particular, the leakage of flammable gases can easily cause secondary combustion and explosions in the factory. Therefore, drones are needed to survey the internal conditions of the factory. However, the existing technology lacks an accurate classification of drone functions and a coordinated planning of drones with different functions. How to enable drone swarms to carry out firefighting work in the early stages of factory fires and reduce the data processing load on servers, and how to coordinate the planning of drone swarms to suppress or even extinguish fires in the early stages of factory fires are technical problems that have not yet been solved by the existing technology.

There are many technical solutions for multi-UAV collaborative path planning in the prior art, which are mainly applied to multi-UAV guided rescue path planning in urban fire scenes, in which UAV guidance is usually used to reduce the danger level of firefighters. For example, the patent document with publication number CN 113985913A discloses a centralized multi-UAV rescue system based on urban fire spread prediction. When a distress occurs, the central layer UAV sends the constructed initial environment model to the execution layer UAV through the first communication module. The execution layer UAV performs search and rescue according to the trapped personnel information in the received initial environment model information, and collects real-time information during the search process. When the current emergency environment information is collected, the current emergency environment information will be fed back to the central layer UAV through the second sensor module in an interrupted manner. The central layer UAV re-predicts the fire and sends the updated environment model to the execution layer UAV through the first communication module again, forming a closed loop, and finally guiding the trapped personnel out of danger. The execution layer UAV in this technical solution includes a second sensor module for collecting real-time environment information to realize the detection and analysis of the current emergency environment information. At this time, the change of the actual environment conditions will be directly reflected in the initial environment model information of the central layer UAV. However, this technical solution requires the central-layer drone to process the impact of the sudden environmental information on the initial environmental model before it can achieve real-time simulation of the sudden situation. This processing method will significantly increase the delay of the actual simulation of the sudden situation. On the other hand, setting the data center that needs to process the ever-changing sudden environmental information on the central-layer drone will significantly increase its energy consumption, which will lead to a significant reduction in the flight time of the central-layer drone, and it will not be able to support its data processing requirements during the entire firefighting mission.

In view of the deficiencies of the prior art, the present invention provides a collaborative firefighting system based on drones, including at least one drone control station and a computing unit. There are several drones in the drone control station. The drone control station and/or several drones are in communication with the computing unit. The computing unit is configured to: pre-simulate several fire locations in a three-dimensional virtual scene to pre-plan the movement routes between the drone and several fire locations based on the three-dimensional virtual scene and the firefighting materials or monitoring equipment carried by the drone; and send the relevant movement routes to the drone or the drone control station in a manner of assigning firefighting tasks to the drone. The present invention pre-simulates possible fire locations and the corresponding firefighting tasks to be performed by the drone in a three-dimensional virtual scene, so that the drone can be deployed for early firefighting work as soon as a fire occurs. The computing unit directly calls the simulation data of the drone movement to control the drone to perform the corresponding firefighting tasks.

According to a preferred embodiment, in the event of a fire or a fire about to occur, the computing unit sends a control command to a drone control station or several drones so that at least two drones monitor the fire location by carrying different monitoring equipment to obtain monitoring data. The computing unit constructs the fire situation of the fire location in a three-dimensional virtual scene based on the monitoring data and adjusts the movement route based on the changes in the monitoring data. In the prior art, there are technical solutions that collect and update building information during the flight of drones to timely reflect the impact of the fire location on the fire environment nearby. For example, the patent document with publication number CN113274663A discloses a control method, device and computing equipment for a firefighting drone, wherein the firefighting drone is controlled to approach the fire point of the building according to the picture taken by the camera of the firefighting drone, and the contour data of the building facade is generated according to the radar signal of the firefighting drone; the firefighting target point is selected according to the picture; the movement instruction of the firefighting drone is generated according to the firefighting target point, the contour data, the direction of the firefighting drone and the field of view of the firefighting drone; wherein the movement instruction is used to control the firefighting drone to fly to the firefighting operation point, and to control the firefighting drone to face the firefighting target point; and to control the firefighting drone to perform firefighting actions from the firefighting operation point to the firefighting target point. In this technical solution, the process of regenerating the contour data of the building is directly realized by the data processing center on the drone. At this time, the processing method of the drone needs to calculate and process the data of the change of the fire to the environment in real time, which consumes a lot of electricity. On the other hand, the drone is still in flight at this time, and its current power information has changed significantly after the fire-fighting action consumption. If the subsequent fire-fighting route planning is still carried out according to the initial energy data, the subsequent fire-fighting task will be suspended midway and the corresponding fire-fighting task cannot be completed as planned, thereby reducing the fire-fighting efficiency of the entire drone group. Compared with the above-mentioned prior art, the calculation unit in the present invention can construct the fire situation of the fire location in a three-dimensional virtual scene based on the monitoring data and adjust the moving route based on the change of the monitoring data. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to adjust the safe moving route of the drone in time according to the real-time changes of the fire environment, and reduce the amount of data calculation of the drone during the flight adjustment process. The present invention can call the corresponding type of monitoring drone according to the three-dimensional virtual scene affected by the fire, so as to realize the construction of the fire situation of the fire location in the three-dimensional virtual scene based on the monitoring data, prevent the failure of the drone caused by the inability to adjust the moving path of the subsequent fire-fighting drone in time due to the spread of the fire, and ensure the safety of the drone when working. Furthermore, in the event of a fire, some of the digital twin sensors in the factory may fail, resulting in omissions in the computing unit's monitoring of the fire location and incomplete data collection of the fire location. The drone scheduling decision made by the computing unit is biased, affecting the firefighting work in the early stages of the fire. In this regard, at least two drones dispatched by the computing unit of the present invention can provide firefighters with intelligence within the factory to ensure that firefighters use the correct equipment and safety equipment to approach the scene for firefighting in subsequent firefighting work. Through the above-mentioned setting, the present invention can use drones carrying monitoring equipment as a replacement for some of the failed digital twin sensors in the factory, thereby solving the problem of incomplete three-dimensional virtual scenes due to fire failures, which in turn leads to reduced accuracy in route planning for drones to perform corresponding firefighting tasks.

According to a preferred embodiment, the computing unit controls at least two drones to be equipped with one of a visible light camera, a thermal imaging camera, and a gas concentration monitoring device to monitor the fire location and obtain monitoring data. When the computing unit obtains the monitoring data of the fire location, the computing unit is also configured to: send a control instruction to the drone control station or the drone so that at least two groups of drones are equipped with corresponding monitoring equipment to monitor the fire location in a cyclic manner to continuously obtain monitoring data. The present invention performs all-round monitoring of the fire location, thereby being able to quickly generate a digital twin model of the fire location in a three-dimensional virtual scene, and the relevant data collected in this way can fully reflect the fire location situation, effectively improving the accuracy of drone scheduling.

According to a preferred embodiment, in response to the acquisition of monitoring data of the fire location, the computing unit determines the cause of the fire at the fire location based on the monitoring data to assign firefighting tasks to drones so that several drones can extinguish the fire in a cyclic and coordinated manner. Due to the limitations of drone energy reserves, drones cannot monitor the fire location for a long time. Therefore, multiple groups of drones are used to cyclically monitor the fire location to avoid the problem of drones being unable to return to the drone control station due to excessive energy loss. In the event of a fire due to a leak of flammable gas, for example, the computing unit avoids dispatching fuel drones to perform corresponding firefighting tasks, thereby preventing the open flame of the fuel drone's generator from igniting the flammable gas.

According to a preferred embodiment, the firefighting mission includes the required moving route of the drone, the type and method of delivering firefighting materials, and the delivery location of the firefighting materials. The drone is configured to: obtain firefighting materials along the moving route to go to the fire location; hover along a preset trajectory at a fixed height, and deliver firefighting materials when the drone coincides with the delivery location. In the case of several drones carrying out firefighting missions, the fire may mutate rapidly, resulting in the need to make targeted changes to the firefighting missions of several drones. Therefore, the computing unit needs to update the firefighting mission according to the sudden change of the fire. Therefore, how to reduce the data processing amount of the computing unit to shorten the update time of the firefighting mission and avoid the drone from hovering in the air or circling in the air for a long time before receiving the firefighting mission is a problem that must be solved in the prior art. The present invention realizes the collaborative obstacle avoidance of multiple drones by planning the way of delivering firefighting materials by drones, without the intervention of the computing unit, thereby improving the perception and control capabilities of the drones.

According to a preferred embodiment, the computing unit is further configured to: in response to the monitoring data sent by the drone, calculate the placement position of the drone's firefighting materials and the corresponding moving route as an updated firefighting task under the change of the fire situation; send the updated firefighting task to at least one drone carrying the same firefighting materials. Several drones carrying the same firefighting materials can be combined into a drone swarm, which can complete complex team firefighting tasks in a collaborative manner by following simple firefighting tasks, and has the characteristics of distribution, self-organization, collaboration, stability, etc. Therefore, the computing unit can control the drone swarm to perform global firefighting only by transmitting the updated firefighting task to a certain drone.

According to a preferred embodiment, after the computing unit issues a firefighting task to at least one drone, at least one drone distributes the firefighting task to several drones that are in communication connection with it. Compared with the above-mentioned prior art, the computing unit of the present invention can use drones as the issuing terminal of firefighting tasks. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to improve the response speed of different drones when performing different firefighting tasks. Specifically, the present invention generates the global behavior of firefighting tasks in a large number of local interactions through the interaction between the computing unit and at least one drone, which greatly improves the data transmission performance while also greatly improving the reliability of data transmission, and avoids the problem of paralysis of the drone control system caused by the overload of the computing unit. Even if some drones do not receive the relevant control instructions sent by the computing unit, they can still perform firefighting tasks through data transmission between drones. Further, the drone of the present invention is configured as: a full mesh network connection is performed between several drones carrying the same firefighting materials, so that each drone can perform point-to-point data transmission with other drones. The above-mentioned full mesh network connection is, for example, a software-defined network technology, which realizes data transmission and information synchronization between several drones by separating forwarding and control, so as to improve the data transmission efficiency between drones.

According to a preferred embodiment, the computing unit is further configured to: in response to the monitoring data sent by the drone, in the case of several fire locations, send a firefighting task of isolating several fire locations to the drone control station and/or at least one idle drone, so that the drone limits the burning of the fire location. The computing unit enables the drone to construct several controlled fire sources in a controlled combustion manner, thereby limiting the spread of the fire location. Compared with the above-mentioned prior art, the computing unit of the present invention can adjust the firefighting task of the drone according to the distribution of multiple fire locations. Based on the above-mentioned distinguishing technical features, the problems to be solved by the present invention may include: how to reduce the spread speed of the fire location. Specifically, due to the influence of the wind direction in the factory, the fire location may spread over a large area, thereby appearing multiple fire locations. The present invention can form a fire line with the fire at the original fire location by making a controlled fire source by the drone, thereby cutting off the supply of combustibles in the fire location, reducing the area of the fire scene, reducing the intensity of the fire, and creating favorable conditions for firefighters. The present invention replaces the traditional manual or mechanical ignition method with a drone, and can accurately control the location and range of the ignition to ensure the safety of firefighters in extinguishing fires.

The present invention also relates to a collaborative firefighting method based on drones, the method comprising: pre-simulating several fire locations in a three-dimensional virtual scene to pre-plan the movement routes between the drone and the several fire locations based on the three-dimensional virtual scene and the firefighting materials or monitoring equipment carried by the drone; and sending the relevant movement routes to the drone or the drone control station in a manner of assigning firefighting tasks to the drone. The present invention pre-simulates fire scenes for factories that have completed digital twins, thereby providing a processing solution for possible future fires and facilitating rapid response to possible future fires by dispatching drones.

According to a preferred embodiment, the method further includes: in the event of a fire or a fire about to occur, sending a control instruction to a drone control station or several drones so that at least two drones monitor the fire location by carrying different monitoring equipment to obtain monitoring data; constructing the fire situation at the fire location in a three-dimensional virtual scene based on the monitoring data and adjusting the moving route based on the changes in the monitoring data. The early firefighting tasks performed by several drones can determine the cause of the fire alarm. The computing unit is responsible for the dispatch of at least two drones in response to the fire alarm. The present invention provides effective intelligence to subsequent firefighters through the early monitoring tasks of drones, which facilitates firefighters to understand the complexity of the fire location.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a simplified application scenario schematic diagram of a cooperative fire fighting system based on a drone according to a preferred embodiment of the present invention;

FIG2 is a schematic diagram of another simplified application scenario of a cooperative fire fighting system based on a drone according to a preferred embodiment of the present invention;

FIG3 is a schematic diagram of a drone circling on a preset trajectory in a drone-based collaborative firefighting system according to a preferred embodiment of the present invention;

FIG4 is a schematic diagram of a drone circling in another preset trajectory of a drone-based cooperative firefighting system according to a preferred embodiment of the present invention;

FIG5 is a simplified module connection diagram of a cooperative fire fighting system based on a drone according to a preferred embodiment of the present invention.

Reference numerals list

100: computing unit; 200: drone control station; 300: drone; 400: fire location; 401: first fire location; 402: second fire location; 500: moving route; 600: preset trajectory; 700: storage warehouse; 800: drone charging station.

Detailed ways

The following is a detailed description with reference to the accompanying drawings.

The present invention explains some of the noun terms.

Three-dimensional virtual scene: Based on the characteristics and data of the real world, a corresponding virtual scene and virtual model are created in a digital environment. In addition to having the same form and characteristics as the entity, the virtual scene and virtual model also have the ability of real-time simulation and prediction, and can simulate and analyze the environment and various factors where the entity is located. In the present invention, the three-dimensional virtual scene is consistent with the buildings and equipment in the factory, so that when a fire occurs, the specific situation of the fire location 400 is simulated in the three-dimensional virtual scene, so that the computing unit 100 can obtain and update the fire situation of the fire location 400 in real time in the three-dimensional virtual scene.

UAV control station 200: It has the ability to monitor and allocate UAV 300 and mission payloads, and includes a set of equipment for launching and recovering UAV 300. UAV control station 200 is a very important component of the entire UAV system. It is a channel for the computing unit 100 to interact directly with the UAV 300. It integrates control, communication, and data processing, including mission planning, mission playback, real-time monitoring, digital maps, and communication data links. It is the command and control center of the entire UAV system.

The computing unit 100 can be a dedicated integrated chip, a server, a central processing unit, or other computing logic components. The computing unit 100 can build a three-dimensional virtual scene based on the digital twin data of the factory, and send control instructions to the drone control station 200 according to the pre-simulated fire treatment plan.

Simulation data: When a fire occurs in a simulated three-dimensional virtual scene by the computing unit 100, several fire locations 400 and corresponding UAV 300 scheduling decisions are made, including the moving route 500 of the UAV 300, the loading status of firefighting materials and/or monitoring equipment, and the placement location, etc.

Monitoring data: fire scene temperature distribution, leakage concentration distribution, ignition point location, etc. related to the fire location 400 monitored by the monitoring equipment carried by the drone 300.

Example 1

In the prior art, the continuous firefighting process of the drone 300 in the case of an early fire in a chemical plant is ignored. The uncontrolled fire in the chemical plant requires the drone 300 or the drone swarm to suppress or even extinguish it. When several different types of drones 300 carry different firefighting materials or monitoring equipment to carry out the early firefighting process during the firefighting process, the temporary planning and assignment of firefighting tasks to several drones 300 not only requires the server to provide a large amount of computing power, but also requires several drones 300 to wait before the firefighting tasks are generated, delaying the opportunity for early firefighting. The planning of the early firefighting strategy of the drone 300 in the prior art obviously does not support the drone 300 to perform efficient firefighting work, especially when the drone 300 performs different firefighting tasks and carries different firefighting materials or monitoring equipment. The decision-making and scheduling methods of the drone 300 in the prior art lack efficient coordination between several drones 300. At the same time, due to the limitation of the on-site network bandwidth, the server in the prior art is also difficult to perform separate line control and real-time monitoring of firefighting tasks for many drones 300, which significantly reduces the firefighting efficiency of the drone 300 or the drone swarm.

As shown in Fig. 5, the drone-based cooperative firefighting system of the present invention includes at least one drone control station 200 and a computing unit 100. In the present invention, the drone control station 200 and the computing unit 100 transmit information via a wireless connection.

Several drones 300 are parked in the drone control station 200. The drones 300 can establish a communication connection with the computing unit 100.

Preferably, the working principle of the calculation unit 100 of the present invention is:

As shown in FIG1 , the computing unit 100 pre-plans the moving route 500 of the drone 300 between the drone control station 200 and several fire locations 400 based on the three-dimensional virtual scene of the factory that has completed the digital twin. The moving route 500 of the drone 300 can be the optimal moving route 500 calculated based on the height and position of each building or equipment in the factory. The computing unit 100 performs a running simulation of the moving route 500 of the drone 300 in the three-dimensional virtual scene, so as to directly call the simulation data of the movement of the drone 300 in the early stage of the factory fire to control the drone 300 to perform the corresponding fire fighting task. The simulation data can be stored in a data module or database that is connected to the computing unit 100 for real-time calling by the computing unit 100. The computing unit 100 sends the relevant moving route 500 to the drone 300 or the drone control station 200 in a manner of assigning the drone 300 a fire fighting task. The drone 300 or the drone control station 200 performs the corresponding fire fighting action according to the received moving route 500 and the fire fighting task. It should be noted that there can also be multiple drone control stations 200 in the factory. The multiple drone control stations 200 can cooperate with each other to work together. For example, the moving route 500 planned by the computing unit 100 can go from the first drone control station to the fire location 400 and finally reach the second drone control station closer to the fire location 400.

Preferably, when several fire locations 400 are pre-simulated in a three-dimensional virtual scene, the computing unit 100 uses an intelligent path algorithm to calculate the forward route and the return route of the drone 300 based on the firefighting materials or monitoring equipment carried by the drone 300 and the digital twin data of the factory.

More preferably, the calculation unit 100 determines the danger range of a certain fire location 400 according to the actual spatial position of a certain fire location 400 in the factory, the distance from the building and equipment, and the current fire situation. The calculation unit 100 calculates the moving route 500 of the drone 300 through an intelligent path algorithm based on the position of the drone control station 200, the remaining energy reserves of several drones 300, the fire-fighting materials or monitoring equipment carried, and the danger range of the fire location 400. There are multiple options for the above-mentioned intelligent path algorithm. The calculation unit 100 can select from the database including ant colony algorithm, reinforcement learning algorithm, mathematical optimization algorithm and curve fitting algorithm to calculate the moving route 500 of the drone 300. The present invention does not elaborate on this.

Since the factory has completed the digital twin, it is equipped with several digital twin sensors to monitor various data inside the factory. When several digital twin sensors in the factory detect that a fire has occurred or is about to occur, the computing unit 100 is configured to send a control instruction to the drone control station 200 or the drone 300 so that at least two drones 300 monitor the fire location 400 from different perspectives by carrying different monitoring equipment to obtain monitoring data. The above monitoring data can be the temperature distribution of the fire scene, the distribution of leaks, the location of the fire point, etc.

The above-mentioned fire occurrence refers to the occurrence of open flames in the factory. The above-mentioned situation of fire occurrence refers to: a large amount of gaseous flammable gas leaks inside the factory, and the flammable gas quickly spreads to other areas due to the influence of wind and air sedimentation in the factory, but since there is no open flame in this area, no fire has occurred yet.

In the event of a fire, some digital twin sensors in the factory, especially those near the fire area, may fail, resulting in omissions in the monitoring of the fire location 400 by the computing unit 100 and incomplete data collection at the fire location 400. The scheduling decision of the drone 300 made by the computing unit 100 is biased, affecting the firefighting work in the early stage of the fire.

In the event of a fire, the special point is that the drone 300 cannot obtain relevant data and conditions of the fire location 400 through a simple visible light camera. For example, in the case of an ammonia leak, the flammable gas is very easy to volatilize into the air and the leakage range of the flammable gas cannot be observed by visible light. If the spreading flammable gas comes into contact with an open flame, the fire will change rapidly within a few seconds to cover all areas where flammable gas exists. The leakage of flammable gas is highly uncertain. In the case of early fire alarm, firefighting is not required only when an open flame appears. If a volatile and invisible flammable gas leaks, it is also a serious fire accident.

In order to conduct a comprehensive monitoring of the fire location 400, the computing unit 100 controls at least two drones 300 to be equipped with a visible light camera, a thermal imaging camera, and a gas concentration monitoring device to monitor the fire location 400 and obtain monitoring data. As a result, the computing unit 100 can quickly generate a digital twin model of the fire location 400 in a three-dimensional virtual scene, and the relevant data collected in this way can fully reflect the situation of the fire location 400, effectively improving the accuracy of the dispatch of the drones 300.

In addition, the early monitoring mission performed by several drones 300 can determine the cause of the fire alarm. The computing unit 100 is the dispatch of at least two drones 300 in response to the fire alarm. The prior art lacks sufficient early monitoring of the cause of the fire alarm. If firefighters perform firefighting without understanding the cause of the fire, it may happen that firefighters wear chemical protective clothing to prevent infection from chemicals and enter the fire location 400 for monitoring, but the fire alarm is actually caused by flammable gas. If the flammable gas contacts an open flame or other ignition source, the life safety of the firefighters will be seriously threatened.

At least two drones 300 dispatched by the computing unit 100 can provide firefighters with intelligence within the factory to ensure that firefighters use the correct equipment and safety gear to approach the scene for firefighting in subsequent firefighting work. The prior art lacks corresponding early monitoring work, resulting in firefighters lacking sufficient intelligence to adjust firefighting tactics and safety equipment accordingly. The different and uncertain risk situations of each hazardous chemical require firefighters to conduct slow investigations in early firefighting work. However, depending on the environment, the slow investigation method will also increase the danger to firefighters. The present invention provides effective intelligence to subsequent firefighters through the early monitoring tasks of the drone 300, which facilitates firefighters to understand the complexity of the fire location 400.

Preferably, when the computing unit 100 obtains the monitoring data of the fire location 400, the computing unit 100 is also configured to: send a control instruction to the drone control station 200 or the drone 300 so that at least two groups of drones 300 are equipped with corresponding monitoring equipment to monitor the fire location 400 in a cyclic manner to continuously obtain monitoring data.

More preferably, the calculation unit 100 determines the cause of the fire according to the monitoring data of the fire location 400 to determine the monitoring equipment required for the drone 300 to monitor the fire location 400. The calculation unit 100 divides the drones 300 equipped with the corresponding monitoring equipment into at least two groups of drones 300 according to their own energy reserves. Preferably, the calculation unit 100 divides the drones 300 with more energy reserves into group A and divides the drones 300 with less energy reserves into group B. The calculation unit 100 performs cyclic monitoring of the fire location 400 by dispatching group A and group B to the fire location 400 alternately. The alternating dispatching method enables one group of drones 300 to monitor the fire location 400, and another group of drones 300 to be repaired or charged at the drone control station 200, so that when the energy reserve of the drone 300 at the fire location 400 is insufficient, it can return to the drone control station 200 for charging, and at the same time, another group of drones 300 is deployed to the fire location 400 for continuous inspection. The above method forms a continuous monitoring process of the fire location 400. The above fire causes include open fire, flammable gas leakage, etc. In the case where the fire cause is open fire, the corresponding monitoring equipment carried by the several drones 300 can be a visible light camera or a thermal imaging camera. In the case where the fire cause is a flammable gas leakage, the corresponding monitoring equipment carried by the several drones 300 can be a thermal imaging camera or a gas concentration monitoring device.

Due to the limitation of the energy reserve of the drone 300, the drone 300 cannot monitor the fire location 400 for a long time, so multiple groups of drones 300 are used to cyclically monitor the fire location 400 to avoid the problem that the drone 300 cannot return to the drone control station 200 due to excessive energy loss. In addition, in the event of a fire due to a flammable gas leak, for example, the computing unit 100 avoids dispatching a fuel drone to perform a corresponding firefighting mission, thereby preventing the open flame of the generator of the fuel drone from igniting the flammable gas.

Preferably, the computing unit 100 can adjust the monitoring parameters of the drone 300 carrying different monitoring equipment based on the speed of change of the fire or the speed of diffusion of the leak. For example, when the fire is large or the leakage spreads over a large area, the computing unit 100 controls the drone 300 to increase the hovering height, monitors the fire location 400 on the ground in a high-altitude situational awareness manner, and the computing unit 100 controls the drone 300 to change the cycle frequency every half an hour to ensure the safety of the drone 300 itself and maintain the continuity of monitoring the fire location 400.

Preferably, in response to the acquisition of monitoring data of the fire location 400 , the computing unit 100 allocates firefighting tasks to the drones 300 so that several drones 300 extinguish the fire in a cyclic coordinated manner.

More preferably, the computing unit 100 assigns different firefighting tasks to several drones 300. The firefighting tasks can be to establish an air communication base station, transport firefighting materials, transport rescue materials, provide night lighting, guide trapped personnel, or monitor the fire location 400. Several drones 300 can respectively perform their assigned firefighting tasks to carry out early firefighting work. When the energy reserve of the drone 300 is low, the drone 300 can return to the drone control station 200 or the drone charging station 800 for energy replenishment, thereby forming an uninterrupted cyclic firefighting dispatch.

Due to the lack of unified dispatch of multiple types of drones 300 in the prior art, the drones 300 cannot carry firefighting materials and monitoring equipment that are helpful for firefighting and rescue. The computing unit 100 of the present invention can generate a firefighting task for coordinated firefighting of several drones 300 based on the fire location 400 determined by the early firefighting task and the related monitoring data, and based on the types of the remaining drones 300 in the drone control station 200.

For example, in the case of fire detection, the firefighting materials that the drone 300 can carry include water, dry powder, sand, fire bombs, etc. The firefighting materials can be extracted and stored in the storage warehouse 700 or the drone control station 200 in a packaged manner that the drone 300 can automatically load. When the fire location 400 is on fire, the drone 300 automatically loads the firefighting materials to go to the fire location 400 to deliver them to the fire source or to the edge of the fire location 400 to deliver them to establish an isolation zone.

Specifically, the firefighting task for the coordinated firefighting of several drones 300 generated by the computing unit 100 may be set as follows.

When the computing unit 100 determines the cause of the fire based on the monitoring data, the drone 300 cooperative firefighting task sent by the computing unit 100 to the drone control station 200 is: the drones 300 in the drone control station 200 are divided into at least three groups. The first group of drones are equipped with radio, 4G/5G and other communication equipment to go to the places where the drones 300 must pass to reach the fire location 400 as air base stations, thereby providing a temporary communication network for the fire scene and ensuring data transmission between drones 300 and rescue personnel. The second and third groups of drones depart from the drone control station 200 and arrive at the firefighting material storage warehouse 700 according to the preset safe route to load the designated firefighting materials. After the drone 300 loads the firefighting materials, the firefighting materials are transported to the fire location 400. The first group of drones can also obtain images in the factory during the movement to send to the computing unit 100, and the computing unit 100 corrects the safe routes of the second and third groups of drones according to the images sent in real time. That is, the computing unit 100 georeferences and stitches the image data acquired by the first group of drones into a three-dimensional virtual scene, so that the computing unit 100 draws the damage of factory buildings or equipment, providing global information and judgment basis for the rescue command of firefighters. The first group of drones is used for communication relay and emergency mapping in the fire scene, providing a basis for the execution of subsequent drone 300 firefighting tasks and rescue work of rescue personnel.

When the second and third groups of drones arrive at the fire location 400, they drop firefighting materials at the designated location and return to the firefighting material storage warehouse 700 to circulate the firefighting materials. The firefighting tasks can be pre-stored by the computing unit 100 and triggered by the cause of the fire.

Preferably, a fourth group of drones can also be docked in the drone control station 200. The fourth group of drones can carry a certain weight of rescue supplies to deliver to the rescuers or trapped persons. For example, the fourth group of drones carry supplies such as lifelines, breathing masks, food, and medicines to deliver to the rescuers or trapped persons. The fourth group of drones can also carry searchlights and loudspeakers to provide lighting for fires at night or in thick smoke, as well as large-scale broadcasts to guide evacuation, thereby improving the vision and safety of rescuers and trapped persons.

The circulating fire-fighting material delivery process performed by the drone 300 will affect the normal flight safety of adjacent drones 300. For example, the drone 300 needs to deliver water, dry powder or sand to the fire source at the fire location 400 to reduce the fire. At this time, no other drones 300 can appear in the space below the drone 300 to avoid collision between the drone 300 and the fire-fighting materials. In addition, the wind direction caused by the fire will also cause deviations in the flight trajectory of the drone 300. If each drone 300 requires the computing unit 100 to provide obstacle avoidance routes, corresponding material delivery locations, and delivery timing settings, the computing amount of the computing unit 100 will be greatly increased, affecting the three-dimensional simulation of the real-time digital twin factory by the computing unit 100.

Preferably, the computing unit 100 is configured to send a firefighting task to at least one drone 300 or a drone control station 200 in response to the acquisition of monitoring data of the fire location 400. The firefighting task includes the required moving route 500 of the drone 300, the type and method of delivering firefighting materials, and the delivery location of the firefighting materials.

The drone 300 is configured to: acquire firefighting supplies along a moving route 500 to go to a fire location 400; hover along a preset trajectory 600 at a fixed height, and drop firefighting supplies when the hovering trajectory coincides with the drop location.

Preferably, after loading firefighting materials, the drone 300 can go to the fire location 400 at different heights on the moving route 500. When the drone 300 is close to the fire location 400, it rises or descends to a fixed height for placing firefighting materials, and sequentially enters the preset trajectory 600. The sequential entry can be carried out by the different numbers of the drones 300, and the data transmission between the drones 300 over a short distance is sequentially entered. That is, the drone 300 with the previous number enters the preset trajectory 600 first. If there are no other drones 300 near the drone 300, it enters the preset trajectory 600 by itself. As shown in Figures 3 and 4, the preset trajectory 600 can be a circular trajectory or an "8"-shaped trajectory. The drones 300 enter the circular trajectory or the "8"-shaped trajectory in sequence to place firefighting materials. The firefighting materials thus placed will not accidentally hit other cruising drones 300, avoiding collision accidents of drones 300.

After the drone 300 enters the preset trajectory 600, the drone 300 can avoid collisions between drones 300 based on the pre-installed artificial potential field method. The artificial potential field method means that the drone 300 regards the surrounding obstacles as a repulsive field, regards the target point of the drone 300 as a gravitational field, and determines the movement direction and speed of the drone 300 by calculating the resultant force on the drone 300. In the present invention, the drone 300 also regards other drones 300 as repulsive fields, and regards the moving route 500 and the location where firefighting materials are placed as gravitational fields, thereby achieving fine-tuning of the flight trajectory of the drone 300, without the need for the computing unit 100 to perform additional calculations and data transmission guidance.

The present invention plans the method of dropping firefighting materials by the drones 300 to achieve coordinated obstacle avoidance of multiple drones 300 without the intervention of the computing unit 100, thereby improving the perception and control capabilities of the drones 300.

In the event of a fire, the computing unit 100 should give priority to calculating monitoring data to provide fire rescue decisions and quickly respond to changes in the fire. The computing unit 100 only provides the firefighting tasks to be performed by the drone 300. For subsequent emergencies required by the drone 300, the drone 300, the drone control station 200 or the drone swarm should perform local interactive processing, thereby reducing the amount of calculation of the computing unit 100. That is, the computing unit 100 of the present invention only provides the moving route 500 and the delivery position (firefighting task) required for the drone 300 to fly. The drone 300 autonomously controls the specific flight trajectory and fine-tuning of the trajectory to achieve automatic take-off, material transportation delivery and return, etc.

When several drones 300 are carrying out firefighting tasks, the fire may mutate rapidly, resulting in the need to change the firefighting tasks of several drones 300 in a targeted manner. Therefore, the computing unit 100 needs to update the firefighting tasks according to the fire mutation situation. Therefore, how to reduce the data processing amount of the computing unit 100 to shorten the update time of the firefighting tasks and prevent the drones 300 from hovering or circling in the air for a long time before receiving the firefighting tasks is a problem that must be solved in the prior art.

Preferably, the computing unit 100 is further configured to: in response to the monitoring data sent by the drone 300, calculate the firefighting material placement position and the corresponding moving route 500 of the drone 300 under the change of the fire situation. The computing unit 100 sends the firefighting material placement position and the corresponding moving route 500 to at least one drone 300 carrying the same firefighting material, so that the drone 300 performs firefighting operations with the updated firefighting task. The drone 300 is configured to: establish a full mesh network connection between several drones 300 carrying the same firefighting material, so that each drone 300 can perform point-to-point data transmission with other drones 300. The above-mentioned full mesh network connection is, for example, software defined network (SDN) technology, which realizes data transmission and information synchronization between several drones 300 by separating forwarding and control.

Preferably, after the computing unit 100 issues a firefighting task to at least one drone 300, the at least one drone 300 distributes the firefighting task to a plurality of drones 300 connected to the at least one drone 300 in a full mesh network. The distribution of firefighting tasks refers to issuing different or the same firefighting tasks to other drones 300 according to the control instructions of the computing unit 100 and dividing them by number.

More preferably, as shown in FIG. 2 , the strong wind causes the fire to spread so that other fire points appear near the first fire location 401. The fire point can be the second fire location 402. The computing unit 100 obtains the changes in the fire and the fire location 400 according to the monitoring data, thereby calculating the updated fire-fighting material delivery position and the corresponding moving route 500. The computing unit 100 transmits the updated fire-fighting material delivery position and the corresponding moving route 500 as a fire-fighting task to the corresponding at least one drone 300. The drone 300 distributes the fire-fighting task to several drones 300 connected to the full mesh network, so that some drones 300 deliver fire-fighting materials to the second fire location 402 according to the updated moving route 500. Other drones 300 deliver fire-fighting materials to the first fire location 401 along the previous or updated moving route 500. As a result, there is no need for the computing unit 100 to conduct comprehensive data interaction with all drones 300, and global control of several drones 300 is achieved only by delegating fire-fighting tasks.

Several drones 300 carrying the same firefighting materials can be combined into a drone swarm, which can complete complex team firefighting tasks in a collaborative manner by following simple firefighting tasks, and has the characteristics of distribution, self-organization, collaboration, stability, etc. Therefore, the computing unit 100 can control the drone swarm to perform global firefighting only by exchanging data with a certain drone 300.

The present invention generates global behavior of firefighting tasks in a large number of local interactions through the interaction between the computing unit 100 and at least one drone 300, which greatly improves the data transmission performance and the reliability of data transmission, and avoids the problem of paralysis of the drone 300 control system due to overload of the computing unit 100. Even if some drones 300 do not receive the relevant control instructions sent by the computing unit 100, they can still perform firefighting tasks through data transmission between drones 300.

Preferably, the computing unit 100 is further configured to: pre-simulate in the three-dimensional virtual model the possible location where the drone 300 may stay after completing the firefighting mission to plan the charging route of the drone 300. The computing unit 100 reserves several charging routes for the drone 300 to the drone charging station 800 in the firefighting mission sent to the drone 300 or the drone control station 200. When the drone 300 determines that its own energy reserves cannot complete the return flight, it goes to the drone charging station 800 to replenish energy based on the charging route preset by the computing unit 100.

This method is a pre-simulation of the charging route of the drone 300, so that when the actual situation occurs, the drone 300 does not need to request the computing unit 100 to plan the route, and can go to the drone charging station 800 along the preset charging route for energy replenishment. More preferably, when the staff arrives at the factory, the staff can set up a drone battery swap station in a safe area close to the moving route 500 of the drone 300, and send the information that the drone battery swap station has been established to the computing unit 100. The computing unit 100 calculates the charging route of the drone 300 to the drone battery swap station based on the location of the drone battery swap station. The computing unit 100 updates the charging route into the firefighting task for transmission to at least one drone 300. In the case where the energy reserve of a drone 300 is low, the drone 300 with the lower energy reserve can obtain the charging route through the full mesh network connection to go to the temporarily open drone battery swap station for battery replacement.

Preferably, the computing unit 100 is also configured to: in response to the monitoring data sent by the drone 300, when several fire locations 400 appear, send a firefighting task to isolate several fire locations 400 to at least one idle drone 300, so that the drone 300 limits the spread of the fire locations 400 in a controlled combustion manner.

More preferably, the computing unit 100 sends a special firefighting task to the drone control station 200 or an idle drone 300 in response to the spread of the fire location 400. The special firefighting task can be that the drone 300 carries an ignition device to orderly ignite the combustibles between at least two fire locations 400, thereby controlling the fire to prevent the fire from spreading.

Due to the influence of the wind direction in the factory, the fire location 400 may spread over a large area, resulting in multiple fire locations 400. At this time, the firefighting work of multiple fire locations 400 cannot be solved by simply placing firefighting materials. When the fire spreads, the fire location 400 will generate an updraft, causing the airflow in other directions to flow to the fire location 400. In particular, for the emergence of multiple fire locations 400, the airflow in the factory all flows to the fire location 400. In this regard, controlled ignition can be performed between multiple fire locations 400, so that the combustibles are burned in advance and the fire spreads toward the fire location 400. When the fire at the fire location 400 comes into contact with the fire ignited by humans, the fire at the fire location 400 can be effectively reduced due to the lack of combustible materials and the exhaustion of oxygen. However, when a fire occurs, it is difficult for firefighters to reach the fire location 400. The drone 300 can quickly reach the difficult-to-reach areas in the fire scene, thereby igniting the combustibles and improving the efficiency of fire extinguishing.

Therefore, the present invention can use the drone 300 to create a controlled fire source so that the ignited fire forms a fire line with the fire at the original fire location 400, thereby cutting off the supply of combustibles in the fire location 400, reducing the area of the fire scene, reducing the intensity of the fire, and creating favorable conditions for firefighters. The present invention uses the drone 300 to replace the traditional manual or mechanical ignition method, and can accurately control the location and range of the ignition to ensure the safety of firefighters in extinguishing fires.

Specifically, the computing unit 100 responds to the appearance of several fire locations 400 to divide the several fire locations 400 into several fire scenes. The computing unit 100 obtains the position of the drone 300 that is idle or has completed the firefighting mission, and calculates the required moving route 500 for the drone 300 to obtain the ignition equipment and go to the intersection of several fire scenes based on the digital twin data of the factory. The above-mentioned ignition equipment can be a sphere filled with potassium permanganate. When potassium permanganate combines with ethylene glycol before dripping, a chemical reaction is triggered, thereby forming a controlled fire. The small size of the sphere enables the drone 300 to accurately control the size and location of the fire.

Throughout the text, the features referred to as “preferably” are merely optional and should not be understood as having to be set. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute limitations on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", all of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims (10)

1. The unmanned aerial vehicle-based collaborative fire control system comprises at least one unmanned aerial vehicle control station (200) and a computing unit (1 OO), wherein a plurality of unmanned aerial vehicles (300) are arranged in the unmanned aerial vehicle control station (200), the unmanned aerial vehicle control station (200) and/or the unmanned aerial vehicles (300) are in communication connection with the computing unit (100), and is characterized in that,

The computing unit (100) is configured to:

Pre-simulating a plurality of fire positions (400) in a three-dimensional virtual scene to pre-plan a moving route (500) between the unmanned aerial vehicle (300) and the plurality of fire positions (400) based on the three-dimensional virtual scene and fire-fighting materials or monitoring equipment carried by the unmanned aerial vehicle (300);

-sending the relevant movement route (500) to the drone (300) or to the drone control station (200) in a manner that gives the drone (300) a fire mission.

2. The unmanned aerial vehicle-based collaborative fire protection system according to claim 1, wherein in case of fire, the computing unit (100) sends control instructions to the unmanned aerial vehicle control station (200) or to several of the unmanned aerial vehicles (300) to cause at least two of the unmanned aerial vehicles (300) to monitor the fire location (400) in a manner of carrying different monitoring devices to obtain monitoring data, wherein,

The computing unit (100) builds a fire situation of the fire location (400) in the three-dimensional virtual scene based on the monitoring data and adjusts the movement route (500) based on a change of the monitoring data.

3. The unmanned aerial vehicle-based collaborative fire protection system according to claim 1 or 2, wherein the computing unit (1O 0) controls at least two unmanned aerial vehicles (300) to respectively mount one of a visible light camera, a thermal imaging camera, and a gas concentration monitoring device to monitor the fire location (400) to acquire the monitoring data, wherein,

In case the computing unit (100) acquires the monitoring data of the fire location (400), the computing unit (100) is further configured to: -sending control instructions to the unmanned aerial vehicle control station (200) or to the unmanned aerial vehicle (300) to cause at least two groups of unmanned aerial vehicles (300) to carry the monitoring device to monitor the fire position (400) in a cyclic manner for continuous acquisition of the monitoring data.

4. A collaborative fire protection system based on unmanned aerial vehicles according to any of claims 1-3, wherein in response to the acquisition of the monitoring data of the fire location (400), the computing unit (1 OO) determines the cause of the fire location (400) based on the monitoring data to assign the fire tasks to the unmanned aerial vehicles (300) such that several unmanned aerial vehicles (300) extinguish the fire in a cyclic collaborative manner.

5. The unmanned aerial vehicle-based collaborative fire protection system according to any of claims 1-4, wherein the fire tasks include a travel route (500) required by the unmanned aerial vehicle (300), the type and manner of delivering fire protection materials, and the delivery location of delivering fire protection materials, wherein,

The drone (300) is configured to: acquiring the fire-fighting material along the moving route (500) to the fire-starting position (400); and (3) hovering along a preset track (600) at a fixed height, and throwing the fire-fighting material under the condition that the unmanned aerial vehicle (300) coincides with the throwing position.

6. The unmanned aerial vehicle-based collaborative fire protection system of any of claims 1-5, wherein the computing unit (100) is further configured to:

-calculating, in response to the monitoring data sent by the unmanned aerial vehicle (300), the launch position of the fire-fighting material of the unmanned aerial vehicle (300) under a change in fire and the corresponding movement route (500) as updated fire-fighting mission;

-sending the updated fire task to at least one of the unmanned aerial vehicles (300) carrying the same fire-fighting material.

7. The unmanned aerial vehicle-based collaborative fire protection system of any of claims 1-6, wherein at least one of the unmanned aerial vehicles (300) assigns the fire protection task to a number of the unmanned aerial vehicles (300) in communication connection therewith after the computing unit (100) issues the fire protection task to at least one of the unmanned aerial vehicles (300).

8. The unmanned aerial vehicle-based collaborative fire protection system of any of claims 1-7, wherein the computing unit (100) is further configured to:

In response to the monitoring data sent by the unmanned aerial vehicle (300), in case of the occurrence of several of the fire locations (400), a fire task isolating several fire locations (400) is sent to the unmanned aerial vehicle control station (200) and/or at least one of the unmanned aerial vehicles (300) that is idle, such that the unmanned aerial vehicle (300) limits the combustion of the fire locations (400).

9. A collaborative fire protection method based on an unmanned aerial vehicle, the method comprising:

Pre-simulating a plurality of fire positions (400) in a three-dimensional virtual scene to pre-plan a moving route (500) between the unmanned aerial vehicle (300) and the plurality of fire positions (400) based on the three-dimensional virtual scene and firefighting materials or monitoring equipment carried by the unmanned aerial vehicle (300);

-sending the relevant movement route (500) to the drone (300) or to a drone control station (200) in a manner that gives the drone (300) a fire mission.

10. The unmanned aerial vehicle-based collaborative fire protection method of claim 9, further comprising:

in case of fire or in the event of a fire, sending control instructions to a drone control station (200) or to several of said drones (300) to cause at least two of said drones (300) to monitor said fire location (400) in a manner to carry different monitoring devices to obtain monitoring data;

-constructing a fire situation of the fire location (400) in the three-dimensional virtual scene based on the monitoring data and-adjusting the movement route (500) based on a change of the monitoring data.